KR100997279B1 - Image processing system and method with dynamically controlled pixel processing - Google Patents

Abstract

It is a system for processing digital images. The system includes a controller that includes a processor and a memory. The system may also include a plurality of image processing blocks operatively coupled with the controller. Each image processing block may be set to perform different image processing operations. The image processing blocks and controllers can be set and interconnected to provide sequential pixel processing, with each image processing block processing an input pixel to produce an output pixel, the output pixel being upstream of the image processing block being imaged Feedforward as an input pixel to be downstream of the processing block. The system may also include a classification block, configured for each image processing block, to obtain updated classification data for an input pixel to be applied to the image processing block. Based on the updated classification data for the input pixels applied to the image processing block, the processing in each image processing block can be dynamically controlled.

Description

Image processing system and method by dynamic control pixel processing {IMAGE PROCESSING SYSTEM AND METHOD WITH DYNAMICALLY CONTROLLED PIXEL PROCESSING}

Many systems and methods exist for processing digital images. Existing image systems generally include processing blocks for performing various operations on pixels containing digital images. These operations include de-interlacing, increasing or decreasing resolution, and the like. Typical existing systems use some, fixed processing algorithm for this operation. Different processing operations operate substantially independently of each other, and processing is not adjusted or modified in response to changed pixel characteristics.

1 schematically illustrates an embodiment of an image processing system according to the present disclosure.

2 illustrates a dynamically controllable image processing block that may be used in connection with the systems and methods herein.

3 illustrates various kinds of processing data and variability classification data that may be associated with a pixel or group of pixels, which data is available as input to the image processing block to adjust or otherwise control the image processing block. .

4 illustrates an example implementation of a method for processing a digital image according to the present disclosure.

5 illustrates an example processing pipeline in accordance with the present disclosure and includes dynamic controllable processing blocks for performing deinterlacing, image interpolation and color processing operations.

6 and 7 illustrate dynamically controllable processing blocks for performing interlaced image frames and deinterlacing operations.

8 illustrates a target pixel and pixel grid to be interpolated from one or more known pixel values in a grid in the context of an image interpolation processing operation.

1 schematically illustrates an embodiment of an image processing system 20 according to the present disclosure. This figure is a schematic illustration, and the components shown may be integrated or combined in various ways or separated into additional components without departing from the scope and spirit of the described system.

The image processing system may include a block 22 for receiving and performing initial processing on an input video signal. Block 22 may be configured to handle analog and / or digital inputs. In the case of an analog image, block 22 may include sub-components for capturing and / or decoding the analog image signal to generate corresponding pixels representing the input image frame. For example, an analog image decoder may be used that includes an analog-to-digital converter (ADC) suitable for producing pixels representing the input image frame. These pixels can be clocked into the processing pipeline or otherwise applied. In a typical embodiment, the pixels are clocked into the system in series.

For analog images, devices such as Philips 7119 can be used to provide the pixels captured by the processing pipeline. Devices such as Analog Devices 9887 can be used to provide pixels captured by the processing pipeline, either through an analog-to-digital converter, or for images captured from a DVI source.

Additionally, or alternatively, block 22 may be set to handle digital image input. In the case of a digital image, a suitable digital image decoder with a capture / decoding block 22 for reconstructing the image frame can be implemented. During the decoding process and at other points during the processing, the classification data may be associated with the pixel based on the method used to reconstruct the pixel. In connection with the embodiments described herein, current digital video decoders from companies such as Conexant (CX22490) or LSI Logic (SC2005) can be used.

System 20 may include block 24 for grouping pixels. After capture, the pixels typically corresponding to the input image frame are assigned to areas of fixed or variable size based on a series of allocation criteria used in block 24. Allocation criteria can vary considerably. Simple spatial allocations may be used, such as rectangles, squares, or other geometric relationships between pixels within a video frame. Based on the likelihood that pixels belong to the same object in the image, such as the face of a person, an object based assignment system can be used to group the pixels. The grouping criterion may consist of spatial blocks such as those used in MPEG video encoding. However, another grouping example is based on an object recognition scheme in which pattern matching is used to group regions of similar pixels.

Indeed, block 24 may group pixels according to any executable criterion. Typically, grouping or regionalizing of these pixels is used to facilitate processing and / or analysis of the pixels by the system 20. For example, in an image of an athletic event with a large number of spectators in the background, the pixels corresponding to this background area may be grouped together (based on shared characteristics). Then, a specific processing step can be applied to the entire grouped pixels, thus improving the efficiency and speed of image processing for the image signal. Moreover, grouping and zoning image data can greatly improve granularity processing and enhance the efficiency of the dynamic, real-time processing systems and methods described herein.

The classification data may be added to the pixel data by block 24 or other part of the system 20, in the form of discrete bits or multi-bit parameters. Discontinuous bits may be used to flag individual characteristics (detected edges, etc.). Multi-bit fields can be used to store numeric values that indicate the amount of characteristics (motion, etc.) present in a pixel.

Although some analysis and classification of pixels may be performed by other portions of system 20, the system may include block 26 to perform classification and analysis of the pixels being processed. Various methods and systems may be used to perform the classification, and analysis may be performed to detect various characteristics associated with the pixels, including motion information; Gradients; Quantization scale factor; Prediction method used; Inverse discrete cosine transform coefficients; Frequency information (spatial and / or temporal frequency); Color information (brightness, contrast, hue, saturation, etc.); Whether the pixel includes text, graphics or other classifiable elements; Whether film mode is used; And the like. This property is generally changed during the processing of the pixel and can be referred to as classification data.

Determination of these properties can be performed using any practical method. For example, frequency information can be obtained by calculating the absolute difference between spatially adjacent pixels. Motion information may be generated by comparing the pixels with the pixels of one or more previous frames to calculate the difference.

As discussed in detail below, grouping block 24 and / or analysis block 26 may be accessed repeatedly during processing of a pixel or group of pixels. Iteratively updating the classification data allows the system to dynamically track the changing characteristics of the processed pixels and react dynamically to these changes in real time, to dynamically control and enhance image processing.

Image processing system 20 also includes one or more image processing blocks 28. Block 28 may be set up to perform various various image processing tasks, such as deinterlacing, image interpolation or other resolution change, color processing, luminance / chrominance separation, noise filtering, boosting, and the like. . The general embodiment uses separate blocks 28 for deinterlacing, image interpolation, and color processing. As described below, the systems and methods of the present invention enable processing in a given processing block, which is dynamically controlled in accordance with granular, changeable classification data.

System 20 may also include a controller 30 to perform and / or support the functions of the remaining described blocks. Controller 30 may include a processor, memory, frame buffer, and / or other components as needed. The components of FIG. 1 may be suitably coupled by a bus or other interconnect 32. The illustrated components may be implemented as a single integrated chip or as a plurality of individual components combined or separated in an executable manner. The controller 30 may be implemented with, for example, a separate processor and a separate memory chip, or the functionality of the blocks 24 and 26 may be combined.

2 and 3, process control in individual process blocks (e.g., deinterlacing blocks) will be described. In FIG. 2, an example image processing block 28 is shown. As can be seen, pixels 100 (or a single pixel) are applied to be processed at block 28. Block 28 processes the pixels 100 according to the control input 102 to output the processed pixels 104 (pixels'). As indicated, the control brought about through the input 102 can occur dynamically, with classification data associated with the pixels 100 (eg, motion data, frequency data, etc.), processing information associated with the pixels 100. (E.g., filter coefficients used in other processing steps, interpolation techniques used in other steps, whether the previous process was tuned to address sharpness issues, etc.), and / or other control parameters. Based on parameters. Classifying and / or processing data from another block (eg, fed from a downstream or upstream block) may be used to control the processing in an instant block.

3 shows more specifically how classification and / or processing of data may be associated with a pixel or pixels. This data can be thought of as a composite field class 120, in which various kinds of data can be associated with pixels processed in a pipeline. Field 122 indicates the pixel with which the remaining data field is associated. The classification data 124 may be associated with a pixel to describe the pixel or characteristics or quality of the pixels. The classification data 124 may include various kinds of information, including the motion data 126, the frequency data 128, the color data 130, and the like. Additionally, or alternatively, class 120 may include processing data 140 that indicates or describes the processing that has already been performed on the pixel or pixels. Process data 140 includes, for example, filtering information 142 from another process block, a parameter or method 144 used during deinterlacing, and the like.

Regardless of how the data is organized or related, the data for a pixel or pixels may include not only current frame data, but also historical data for the pixel (eg, data from a previous image frame). The classification data and / or processing data of the previous or subsequent pixels may be supplied to act on the processing in a given processing block. In addition, the classification and processing data can change dynamically while pixels move through the processing pipeline. This dynamically changing control data can be used to improve image processing through a mechanism that dynamically feeds the change control data forward and / or backward in the processing pipeline. This yields feedforward and feedback effects that are dynamic for image processing of other pixels, or image processing of the same pixel in subsequent processing blocks.

4, a review of an exemplary image processing method 200 is now made. From the description below, it is apparent that the method 200 may be usefully implemented in connection with the systems and components described above. However, this example method, or aspect thereof, may be implemented regardless of the specific embodiments discussed herein.

As shown at 202, the method 200 includes receiving or otherwise obtaining an input pixel to be processed. This may be achieved through the decode feature described above and the analog / digital capture described above (eg, capture / decode block 22 of FIG. 1). The received pixels may then be appropriately grouped or reconstructed at 204, as discussed above with reference to FIG. 1. Further, as shown at 206, the pixels can be analyzed to obtain the desired classification data. Such classification data may include any of the pixel classifiers discussed above, including motion data, frequency data, color information, gradient data, and the like. The grouping and analysis of steps 204 and 206 may be referred to as a frontend operation or task, as in this example is performed prior to other image processing of the pixels (eg, prior to deinterlacing, image interpolation operations, etc.).

At 208, the method includes performing an image processing operation (eg, deinterlacing, image interpolation, noise filtering, etc.) on the input pixel. As discussed above, processing operations may be dynamically controlled in accordance with classification data and / or processing data associated with the pixels (eg, classification data 124 and processing data 140 of FIG. 3).

One use of classification data to dynamically tune image processing operations can be understood in the context of deinterlacing. In the present system, the deinterlacing method used at any given point may depend heavily on the degree of motion detected in the pixel to be processed. As described above, motion can be detected by evaluating temporal changes for pixels occurring across a plurality of image frames. This motion information will then be associated with the pixel, for example, through the use of a multi-field class, such as class 120. The motion information embedded in the class field will then be used for dynamically controlling the deinterlacing operation and / or selecting an appropriate deinterlacing algorithm. Some deinterlacing operations may be suitable for pixels with a high degree of motion, and other deinterlacing operations (or modified versions of the first operation) may be more suitable for regions of an image or for fixed pixels.

The processing in step 208 may also be dynamically controlled based on the prior processing of the pixels supplied to the processing operation. For example, associated process data (eg, process data 140) may indicate that a particular algorithm known to produce a blurring effect in motion has been applied to the pixel. This knowledge can be used to adjust instant processing operations to improve the sharpness of certain pixels, such as the edges of moving objects.

Classification data or processing data associated with other processing operations or pixels other than those processed in step 208 may also be used to control image processing operations in step 208. As shown in FIG. 4, after various post processing operations steps (eg, at 210, 212, 214, and 216), another processing operation may be performed at 210. Figure 1 shows a manner similar to pipeline processing in which a number of different processing operations (ie, corresponding to different image processing blocks 28) can be performed in the desired sequence. In the method of FIG. 4, for each pass of step 208, a different processing operation may be performed. For example, a deinterlacing operation may be performed in the first pass, and image interpolation, color processing, and noise filtering may be performed in the subsequent pass.

For a given processing operation, classification data or processing data occurring in one of the other processing operations in the pipeline may be used to act on the processing operation. In pipelines that perform deinterlacing, image interpolation, and color processing operations, for example, classification data for output pixels from image interpolation processing may be used to control the deinterlacing process. In this setting, analysis of pixels resulting from image interpolation processing may reveal issues of image quality that are best addressed as adjustments to the deinterlacing processing parameters. Process data can also be feedforward or feedbackword through operations in the pipeline. In the above example, the processing data from the image interpolation block can demonstrate repeated use of filter constants to improve sharpness. This processing data can be feedforward or feedback word (upstream or downstream) through the pipeline, and if the sharpness can be handled more efficiently in other parts of the pipeline, the processing task moves to another block.

4, after the selected processing operation, output pixels resulting from the processing operation may be reanalyzed and / or regrouped at 210. Typically, the classification data for a pixel or pixels changes as a result of the applied processing operation, such as frequency information changing, gradient changing, motion vector changing, and so on. The classification data for the pixel or pixels may then be updated at 212. Additionally, or optionally, processing information for the pixel may be updated at step 212. Indeed, by updating relevant fields of a multi-field class, such as for example class 120 (FIG. 3), any classification or processing information associated with a pixel can be updated.

From the above description, as a pixel passes through the processing pipeline, the classification and processing data for a given pixel or pixels may change dynamically, such as the pixel characteristics changing, and different processing parameters and algorithms applied during processing. have. This varying classification / processing information can be feedforwarded and feedbackworded through the processing pipeline to dynamically adjust processing operations occurring at any point in the system. In practice, in step 214, the updated classification / processing information resulting from the processing operation just completed (step 208) is passed to the desired portion of the processing pipeline, with potential feedforward and feedback effects on the image processing operation. At 216, if additional processing operations are to be performed on the pixel (eg, in a downstream block of the processing pipeline), the method 200 returns to step 208 to perform the next selected processing operation.

If no further processing operations are performed, "back-end analysis" and comparison may be performed at 220 and 222. This may include performing additional analysis to obtain updated classification information for the final output pixel. The results of this backend analysis can be compared with the frontend data obtained at 204 and 206 to more dynamically adjust or control any processing operation occurring within the processing pipeline. In the context of the example system of FIG. 1, the characteristics of the initial input pixel may be compared with the classification of the pixel that contains the final output image frame to assess whether a processing goal has been achieved. This comparison will then be used to dynamically adjust the processing operations performed by the processing block 28 in the image processing pipeline, as shown at 224. After the processing, an image frame is output as shown at 226.

Typical embodiments of the described image processing systems and methods include deinterlacing, image interpolation, and color processing operations. These operations may be performed sequentially in the processing pipeline, as shown schematically in FIG. 5. As discussed above, an input pixel is applied to each block, and the associated processing operations are dynamically controlled based on classification information and / or processing information that typically changes as the pixel is processed and passes through the processing pipeline.

As discussed above, typical embodiments of the systems and methods described include deinterlacing blocks or processing operations. Many video signals are typically provided in an interlaced format, in which horizontal lines across one of the image scenes are scanned and transmitted for a given video frame. Even and odd scan lines appear alternately in succession in an image frame. As a result, in a system in which an image of 60 frames per second is displayed, an image frame including even lines is displayed 30 times, and an image frame including odd lines is displayed 30 times. In this interlaced signal, a given image frame only contains 50% vertical resolution.

With reference to FIG. 6, an operation of an exemplary deinterlacing block in which an interlaced image frame is converted into a signal having full vertical resolution is described. Frames 260 and 262 are video frames of interlaced video signals. As indicated, frame 262 may be referred to as the current frame, while frame 260 may be referred to as the previous frame. Each frame includes a plurality of pixels, denoted by {row, column}, indicating the row and column positions of the pixels within the frame.

Various methods can be used to construct a frame with full vertical resolution. The missing row of the current frame can be simply obtained from the previous frame and added in a manner known as field meshing. Meshing can provide a high quality deinterlaced image, especially if the pixels involved exhibit a static or small degree of motion. Additionally, or alternatively, various kinds of interpolation may be used, in which the target pixel is interpolated based on the attributes of one or more adjacent pixels. For example, adjacent pixels {1,2} and {3,2} or pixels {1,1}, {1,2}, {1,3}, {3,1}, {3,2} and { Missing pixels {2,2 in current frame 262 by averaging or otherwise interpolating the properties of a larger set of adjacent pixels (e.g., brightness, hue, saturation, etc.) } Can be interpolated.

FIG. 7 is a diagram that may be configured to receive an input pixel 282, perform a deinterlacing operation on a pixel based on an applied control signal 284, and output the processed pixel 286 in a deinterlaced format. An exemplary deinterlacing block 280 in accordance with the specification is shown. Deinterlacing block 280 may be implemented in a system such as that shown in FIG. 1, which may be one of the processing blocks 28 in the processing pipeline of FIG. 1.

Similar to the processing block described with reference to FIG. 2, the particular processing operation or method (eg, deinterlacing) performed by block 280 may be in real time according to the classification and / or processing data associated with the input pixel 282. Can be changed dynamically. Additionally, or alternatively, other processing blocks in the pipeline, and classification and / or processing data associated with pixels other than pixel 282 may be used to dynamically change the deinterlacing operation. For example, the choice between interpolation and field meshing for reconstructing missing pixels can be largely determined by motion classification data. Meshing may cause "tearing" or "feathering" effects due to temporal shifts occurring between successive interlaced image frames, so meshing is desirable for moving pixels. Not. Interpolation may be more desirable for pixels with a high degree of motion.

In contrast, static or relatively static images may be more suitable for deinterlacing using non-interpolated methods such as field meshing. Meshing in some cases may produce sharper images, and may be more desirable for deinterlacing small images. Exemplary block 280 not only selects between interpolated and non-interpolated methods, but also mixes these methods with the desired appropriate weights based on the classification and / or processing data or other parameters embedded in control signal 284. It may be set. In the example shown, the control signal can cause the placement of a pure meshing method, a pure interpolation method, or any mixture of two extremes.

Any number of deinterlacing methods may be selected or selectively combined based on the classification data and / or process data, including field mixing into the FIR filter, using median filters, line doubling, The use of vertical time filters, averaging filters, and the like. Generalizing to an interlacing processing block with N alternating deinterlacing methods or algorithms, the system alternates in any desired manner based on the abundant control data available in the processing data and / or classification data. Cross-fades or combinations between the methods can be used. Certain alternating methods may be weighted or highlighted higher than other methods, and one particular method may be chosen to exclude others. That is, classification data and / or processing data may be used to control the degree to which each available deinterlacing method participates in the deinterlacing process for generating the target pixel or pixels.

This example of FIG. 7 may be used to illustrate how classification data and processing data are feedforward and / or feedbackword to dynamically adjust processing in real time. Assume that input pixel 282 is from a particular region of an image frame, and that classification data associated with pixel 282 represents a high degree of motion in that portion of the image frame. The processing at deinterlacing block 282 then dynamically constructs a complete vertical resolution using even higher weighted methods, perhaps purely interpolated methods, to avoid feathering or other unwanted artifacts. Can be adjusted.

As discussed above, interpolated deinterlacing methods can cause blurring effects or other loss of sharpness. Continuing with the example described above, if a loss of sharpness occurred due to the use of interpolation during deinterlacing, this would be reflected in the classification data obtained for the output pixel (e.g., in the analysis / classification block 26 of Figure 1). due to). The associated classification data will flow downstream to reach the next processing block, which will cause a lack of sharpness in adjusting the processing algorithm. In alternate embodiments, classification data will be sent upstream.

Additionally or alternatively, information about the deinterlacing operation itself may be reported upstream or downstream. In this example, the reported processing information will indicate that a high degree of interpolation was used for deinterlacing. Other processing operations may be dynamically adjusted in response to compensation for sharpness loss resulting from the deinterlacing operation.

The classification and / or processing data may be supplied upstream or downstream to control the processing (image interpolation) or processing blocks that change the resolution of the input pixel. The resolution change may be differently applied to different regions of the input image frame, and may include a reduction in resolution and / or an increase in resolution (upconversion). The methods used to change the resolution may be dynamically controlled based on the input classification and / or processing data. Typically, dynamic control results in a dynamic change in the image scaling factor used to obtain the target pixel. Dynamic control of the coefficients can be used whether the scale of the image is large or small, and can also be used with linear and nonlinear methods.

For example, upconversion can be achieved by sampling the input pixels and applying the sampled values to a grid of new, larger pixels, which process, although interpolation is generally used, can Pixel replication using a " nearest neighbor " One common method is cubic convoluting interpolation, using multiple coefficient filters. Referring to FIG. 8, a pixel grid is shown. In the middle of the grid is the target pixel whose value must be determined. Interpolation can determine this pixel by evaluating the values of adjacent pixels. The value of an adjacent pixel can be considered along with the distance from the target pixel.

In practice, cubic line interpolation involves interpolating based on four known pixels. For example, in the horizontal direction of FIG. 8, the target pixel may be configured with known pixel values {2,0}, {2,1}, {2,3} and Can be interpolated from {2,4}. Image scaling coefficients can also be used to filter out noise or other high frequency artifacts in an upconverted image and to give higher weights to particular pixels. Interpolation can typically be applied in both the horizontal and vertical directions to determine the value of the target pixel.

Classification data and processing data related to pixels, or from other sources, can be used to dynamically adjust image interpolation. Interpolation coefficients may be determined based on or according to motion, gradient, and / or frequency information associated with the input pixel. If existing processing algorithms provided sub-optimal sharpness enhancement, filter coefficients may be selected for image interpolation to improve or preserve sharpness in portions of the image.

The dynamic control and feedforward and feedback features discussed herein are equally applicable to color processing and other image processing operations. In the context of color processing, changes in the classification data and processing data associated with an input pixel may be used to select, adjust, or control the algorithm used to change the brightness, contrast, hue, saturation, color space conversion, etc. of the input pixel. Can be used. The overall brightness of the pixel may be reduced in response to motion information for the pixel. Motion history for a pixel or pixels can be used to identify and correct a workpiece associated with an occlusion problem. In addition to or instead of the control being based on data related to the input pixel, the control may be based on classification or processing data supplied from other portions of the processing pipeline (via feedback or feedforward settings).

 In addition to, or instead of, controlling the control based on the data associated with the input pixel, the control may be based on classification or processing data supplied from another portion of the processing pipeline (via feedback or feedforward configuration).

While the embodiments and method implementations have been shown and described in detail, those skilled in the art will recognize that various modifications may be made without departing from the spirit and scope of the invention. This specification includes all unclear and new combinations of the described elements, and the claims represent any unclear and new combination of these elements in this or a later application. Where a claim describes one element, or first element, or equivalent, such claim includes a mixture of one or more such elements and does not require or exclude two or more such elements.

Claims (28)

As a system for processing digital images, A controller including a processor and a memory; A plurality of image processing blocks operatively coupled with the controller, each image processing block being set to perform a different image processing operation, wherein the image processing blocks and the controller are set to be interconnected to provide sequential pixel processing Each image processing block processes an input pixel to produce an output pixel, and an output pixel of which an upstream of the image processing blocks is downstream of the image processing blocks A plurality of image processing blocks, feed forward as; And For each of the image processing blocks, it is set to obtain updated classification data for an input pixel applied to the image processing block, and processing in each image processing block is updated for the input pixel applied to the image processing block. And a classification block, dynamically controlled based on the classification data. The method of claim 1, wherein for at least one of the image processing blocks, the controller is configured to dynamically control processing by selectively combining a plurality of different processing techniques associated with the image processing block, the optional combination being And based on the updated classification data of the input pixel applied to the corresponding image processing block. The image processing system of claim 2, wherein the controller is configured to dynamically control processing occurring in another of the image processing blocks based on an optional combination of processing techniques in at least one of the image processing blocks. The image processing of claim 1, wherein the controller is configured to dynamically change sharpness control applied to one of the image processing blocks based on a processing technique applied to another of the image processing blocks. system. The image interpolator block of claim 1, wherein one of the image processing blocks is the image interpolator block set to change the resolution of the input pixel applied to the image interpolator block, and wherein the image interpolator block dynamically controls processing. And including dynamically changing an image scaling factor used to obtain the output pixel of the image interpolator block. The image processing system of claim 1, wherein the image processing blocks comprise a de-interlacing block, an image interpolator block set to cause a resolution change, and a color processing block. The image processing system of claim 6, wherein the processing at the image interpolator block is dynamically controlled in response to the processing at the deinterlacing block. The image processing system of claim 6, wherein the processing in the color processing block is dynamically controlled in response to the processing in the image interpolator block. The image processing system of claim 1, wherein the updated classification data includes motion data associated with the input pixel. The image processing system of claim 1, wherein the updated classification data includes frequency data associated with the input pixel. The image processing system of claim 1, wherein the updated classification data includes color data associated with the input pixel. As a system for processing digital images, A controller including a processor and a memory; And A plurality of image processing blocks operatively coupled with the controller, each image processing block being set to perform a different image processing operation, wherein the image processing blocks and the controller are set to be interconnected to provide sequential pixel processing; Each image processing block processes an input pixel to produce an output pixel, and wherein an output pixel of an upstream of the image processing blocks is feedforward as an input pixel to a downstream of the image processing blocks. Including image processing blocks, And the controller is configured to dynamically control the image processing operation performed in one of the image processing blocks based on the image processing operation performed in another of the image processing blocks. The method of claim 12, wherein the controller is configured to dynamically control the image processing blocks in response to classification data associated with a pixel processed by the image processing blocks, wherein the classification data is such that the pixel is one image processing block. Varying from one to the other, and the controller is configured to respond to this change in the classification data when dynamically controlling the image processing blocks. The method of claim 13, wherein for at least one of the image processing blocks, the controller is configured to dynamically control the processing by selectively combining a plurality of different processing techniques associated with the image processing block, the optional combination being And based on the updated classification data of the input pixel applied to the corresponding image processing block. The method of claim 12, wherein one of the image processing blocks is the image interpolator block set to change the resolution of the input pixel applied to the image interpolator block, wherein the controller is performed on another of the image processing blocks. Based on the image processing operation, configured to dynamically change an image scaling factor used to obtain the output pixel of the image interpolator block. 13. The image processing system of claim 12, wherein the controller is configured to dynamically change sharpness control applied to one of the image processing blocks based on the image processing operation performed at another of the image processing blocks. As a method of processing digital images, Receiving an input pixel; Obtaining classification data associated with the input pixel; Performing a first image processing operation on the input pixel, wherein the first image processing operation is dynamically controlled based on the classification data obtained for the input pixel. ; Analyzing the output pixel output from the first image processing operation to obtain updated classification data associated with the pixel output from the first image processing operation; And Performing a second image processing operation on the pixel output from the first image processing operation, wherein the second image processing operation is dynamically controlled based on the updated classification data. Performing an image processing method. The image of claim 17, wherein the first image processing operation and the second image processing operation are dynamically controlled based on motion data associated with pixels processed by the first image processing operation and the second image processing operation. Treatment method. The image of claim 17, wherein the first image processing operation and the second image processing operation are dynamically controlled based on frequency data associated with pixels processed by the first image processing operation and the second image processing operation. Treatment method. The image of claim 17, wherein the first image processing operation and the second image processing operation are dynamically controlled based on color data associated with pixels processed by the first image processing operation and the second image processing operation. Treatment method. As a method of processing digital images, Receiving an input pixel; Performing a first image processing operation on the input pixel; And Performing a second image processing operation, wherein the second image processing operation is dynamically controlled based on processing data associated with the first image processing operation, wherein the processing data is configured to perform the first image processing operation; Performing a second image processing operation comprising information about the processing method used. 23. The method of claim 21, Outputting an output pixel from the first image processing operation; Obtaining updated classification data associated with the output pixel; And And dynamically controlling the second image processing operation based on the updated classification data. 23. The method of claim 22, wherein acquiring the updated classification data includes acquiring motion data associated with the output pixel. 24. The method of claim 23, wherein dynamically controlling the second image processing operation comprises dynamically changing image scaling coefficients associated with the second image processing operation based on the motion data. 24. The method of claim 23, wherein dynamically controlling the second image processing operation comprises: dynamically and selectively combining a plurality of alternate processing methods associated with the second image processing operation based on the motion data. Image processing method comprising a. The method of claim 22, wherein acquiring the updated classification data comprises acquiring frequency data associated with the output pixel. The method of claim 22, wherein acquiring the updated classification data comprises acquiring color data associated with the output pixel. 23. The method of claim 22, wherein the updated classification data includes historical data associated with the output pixel.

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